Overview

Dataset statistics

Number of variables39
Number of observations9608
Missing cells199392
Missing cells (%)53.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory284.0 B

Variable types

Text18
Categorical6
Unsupported9
Numeric2
Boolean4

Alerts

Password [Required] has constant value ""Constant
Org Unit Path [Required] has constant value ""Constant
Home Address has constant value ""Constant
2sv Enforced [READ ONLY] has constant value ""Constant
Storage limit [READ ONLY] has constant value ""Constant
Advanced Protection Program enrollment has constant value ""Constant
Gemini Limit Status [READ ONLY] has constant value ""Constant
Gemini Last Usage [READ ONLY] has constant value ""Constant
Mobile Phone is highly overall correlated with Status [READ ONLY]High correlation
Status [READ ONLY] is highly overall correlated with Mobile PhoneHigh correlation
Status [READ ONLY] is highly imbalanced (97.8%)Imbalance
2sv Enrolled [READ ONLY] is highly imbalanced (91.4%)Imbalance
Change Password at Next Sign-In is highly imbalanced (65.7%)Imbalance
Password Hash Function [UPLOAD ONLY] has 9608 (100.0%) missing valuesMissing
New Primary Email [UPLOAD ONLY] has 9608 (100.0%) missing valuesMissing
Recovery Email has 8735 (90.9%) missing valuesMissing
Home Secondary Email has 9603 (99.9%) missing valuesMissing
Work Secondary Email has 9604 (> 99.9%) missing valuesMissing
Recovery Phone [MUST BE IN THE E.164 FORMAT] has 8236 (85.7%) missing valuesMissing
Work Phone has 9589 (99.8%) missing valuesMissing
Home Phone has 9586 (99.8%) missing valuesMissing
Mobile Phone has 9545 (99.3%) missing valuesMissing
Work Address has 9608 (100.0%) missing valuesMissing
Home Address has 9607 (> 99.9%) missing valuesMissing
Employee ID has 9603 (99.9%) missing valuesMissing
Employee Type has 9605 (> 99.9%) missing valuesMissing
Employee Title has 9602 (99.9%) missing valuesMissing
Manager Email has 9608 (100.0%) missing valuesMissing
Department has 9605 (> 99.9%) missing valuesMissing
Cost Center has 9608 (100.0%) missing valuesMissing
Building ID has 9608 (100.0%) missing valuesMissing
Floor Name has 9608 (100.0%) missing valuesMissing
Floor Section has 9608 (100.0%) missing valuesMissing
New Status [UPLOAD ONLY] has 9608 (100.0%) missing valuesMissing
Email Address [Required] has unique valuesUnique
Password Hash Function [UPLOAD ONLY] is an unsupported type, check if it needs cleaning or further analysisUnsupported
New Primary Email [UPLOAD ONLY] is an unsupported type, check if it needs cleaning or further analysisUnsupported
Work Address is an unsupported type, check if it needs cleaning or further analysisUnsupported
Manager Email is an unsupported type, check if it needs cleaning or further analysisUnsupported
Cost Center is an unsupported type, check if it needs cleaning or further analysisUnsupported
Building ID is an unsupported type, check if it needs cleaning or further analysisUnsupported
Floor Name is an unsupported type, check if it needs cleaning or further analysisUnsupported
Floor Section is an unsupported type, check if it needs cleaning or further analysisUnsupported
New Status [UPLOAD ONLY] is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-06 22:35:10.069322
Analysis finished2024-05-06 22:35:13.809754
Duration3.74 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Distinct9304
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
2024-05-06T15:35:14.036248image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length47
Median length10
Mean length10.774771
Min length1

Characters and Unicode

Total characters103524
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9237 ?
Unique (%)96.1%

Sample

1st row8555011862
2nd row9182018896
3rd row1
4th row10
5th row11
ValueCountFrequency (%)
kumar 162
 
1.4%
aitam 116
 
1.0%
sai 106
 
0.9%
ide 90
 
0.8%
rao 82
 
0.7%
dr 45
 
0.4%
sri 36
 
0.3%
krishna 34
 
0.3%
kiran 27
 
0.2%
venkata 26
 
0.2%
Other values (9310) 11054
93.9%
2024-05-06T15:35:14.499976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 16184
15.6%
1 11134
 
10.8%
2 10212
 
9.9%
0 8990
 
8.7%
5 8936
 
8.6%
4 4231
 
4.1%
3 3589
 
3.5%
8 3431
 
3.3%
9 2279
 
2.2%
- 2194
 
2.1%
Other values (61) 32344
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 16184
15.6%
1 11134
 
10.8%
2 10212
 
9.9%
0 8990
 
8.7%
5 8936
 
8.6%
4 4231
 
4.1%
3 3589
 
3.5%
8 3431
 
3.3%
9 2279
 
2.2%
- 2194
 
2.1%
Other values (61) 32344
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 16184
15.6%
1 11134
 
10.8%
2 10212
 
9.9%
0 8990
 
8.7%
5 8936
 
8.6%
4 4231
 
4.1%
3 3589
 
3.5%
8 3431
 
3.3%
9 2279
 
2.2%
- 2194
 
2.1%
Other values (61) 32344
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 16184
15.6%
1 11134
 
10.8%
2 10212
 
9.9%
0 8990
 
8.7%
5 8936
 
8.6%
4 4231
 
4.1%
3 3589
 
3.5%
8 3431
 
3.3%
9 2279
 
2.2%
- 2194
 
2.1%
Other values (61) 32344
31.2%
Distinct8179
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
2024-05-06T15:35:14.789235image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length42
Median length35
Mean length14.53622
Min length1

Characters and Unicode

Total characters139664
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7782 ?
Unique (%)81.0%

Sample

1st rowPisini Mounika
2nd rowRaj Kumar potnuru
3rd rowADIREDDI LOKESH
4th rowBUDDA SAI KIRAN
5th rowBUNGA KALYANRAM
ValueCountFrequency (%)
kumar 678
 
3.4%
sai 534
 
2.7%
rao 379
 
1.9%
cse 202
 
1.0%
ece 183
 
0.9%
krishna 140
 
0.7%
pavan 116
 
0.6%
potnuru 113
 
0.6%
me 109
 
0.5%
eee 103
 
0.5%
Other values (5588) 17388
87.2%
2024-05-06T15:35:15.296873image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 27199
19.5%
10357
 
7.4%
I 9371
 
6.7%
R 8307
 
5.9%
N 7875
 
5.6%
S 6156
 
4.4%
H 6118
 
4.4%
U 5651
 
4.0%
E 5626
 
4.0%
L 4817
 
3.4%
Other values (56) 48187
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 27199
19.5%
10357
 
7.4%
I 9371
 
6.7%
R 8307
 
5.9%
N 7875
 
5.6%
S 6156
 
4.4%
H 6118
 
4.4%
U 5651
 
4.0%
E 5626
 
4.0%
L 4817
 
3.4%
Other values (56) 48187
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 27199
19.5%
10357
 
7.4%
I 9371
 
6.7%
R 8307
 
5.9%
N 7875
 
5.6%
S 6156
 
4.4%
H 6118
 
4.4%
U 5651
 
4.0%
E 5626
 
4.0%
L 4817
 
3.4%
Other values (56) 48187
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 27199
19.5%
10357
 
7.4%
I 9371
 
6.7%
R 8307
 
5.9%
N 7875
 
5.6%
S 6156
 
4.4%
H 6118
 
4.4%
U 5651
 
4.0%
E 5626
 
4.0%
L 4817
 
3.4%
Other values (56) 48187
34.5%
Distinct9608
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
2024-05-06T15:35:15.532247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length53
Median length31
Mean length31.2245
Min length22

Characters and Unicode

Total characters300005
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9608 ?
Unique (%)100.0%

Sample

1st row08555011862@adityatekkali.edu.in
2nd row09182018896@adityatekkali.edu.in
3rd row20a55a0301@adityatekkali.edu.in
4th row20a55a0310@adityatekkali.edu.in
5th row20a55a0311@adityatekkali.edu.in
ValueCountFrequency (%)
08555011862@adityatekkali.edu.in 1
 
< 0.1%
16a51a0459@adityatekkali.edu.in 1
 
< 0.1%
17284-ec-023@adityatekkali.edu.in 1
 
< 0.1%
16a51a0244@adityatekkali.edu.in 1
 
< 0.1%
20a55a0301@adityatekkali.edu.in 1
 
< 0.1%
20a55a0310@adityatekkali.edu.in 1
 
< 0.1%
20a55a0311@adityatekkali.edu.in 1
 
< 0.1%
20a55a0312@adityatekkali.edu.in 1
 
< 0.1%
20a55a0313@adityatekkali.edu.in 1
 
< 0.1%
20a55a0314@adityatekkali.edu.in 1
 
< 0.1%
Other values (9598) 9598
99.9%
2024-05-06T15:35:15.967052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 44847
14.9%
i 29877
 
10.0%
e 21459
 
7.2%
d 20053
 
6.7%
t 19816
 
6.6%
k 19521
 
6.5%
. 19483
 
6.5%
1 13990
 
4.7%
2 11459
 
3.8%
5 11070
 
3.7%
Other values (30) 88430
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 44847
14.9%
i 29877
 
10.0%
e 21459
 
7.2%
d 20053
 
6.7%
t 19816
 
6.6%
k 19521
 
6.5%
. 19483
 
6.5%
1 13990
 
4.7%
2 11459
 
3.8%
5 11070
 
3.7%
Other values (30) 88430
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 44847
14.9%
i 29877
 
10.0%
e 21459
 
7.2%
d 20053
 
6.7%
t 19816
 
6.6%
k 19521
 
6.5%
. 19483
 
6.5%
1 13990
 
4.7%
2 11459
 
3.8%
5 11070
 
3.7%
Other values (30) 88430
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 44847
14.9%
i 29877
 
10.0%
e 21459
 
7.2%
d 20053
 
6.7%
t 19816
 
6.6%
k 19521
 
6.5%
. 19483
 
6.5%
1 13990
 
4.7%
2 11459
 
3.8%
5 11070
 
3.7%
Other values (30) 88430
29.5%

Password [Required]
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
****
9608 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters38432
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row****
2nd row****
3rd row****
4th row****
5th row****

Common Values

ValueCountFrequency (%)
**** 9608
100.0%

Length

2024-05-06T15:35:16.265253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T15:35:16.414726image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
9608
100.0%

Most occurring characters

ValueCountFrequency (%)
* 38432
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
* 38432
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
* 38432
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
* 38432
100.0%

Password Hash Function [UPLOAD ONLY]
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9608
Missing (%)100.0%
Memory size75.2 KiB

Org Unit Path [Required]
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
/
9608 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9608
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row/
2nd row/
3rd row/
4th row/
5th row/

Common Values

ValueCountFrequency (%)
/ 9608
100.0%

Length

2024-05-06T15:35:16.531441image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T15:35:16.668080image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
9608
100.0%

Most occurring characters

ValueCountFrequency (%)
/ 9608
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 9608
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 9608
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 9608
100.0%

New Primary Email [UPLOAD ONLY]
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9608
Missing (%)100.0%
Memory size75.2 KiB

Status [READ ONLY]
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
Active
9587 
Suspended
 
21

Length

Max length9
Median length6
Mean length6.006557
Min length6

Characters and Unicode

Total characters57711
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowActive
2nd rowSuspended
3rd rowActive
4th rowActive
5th rowActive

Common Values

ValueCountFrequency (%)
Active 9587
99.8%
Suspended 21
 
0.2%

Length

2024-05-06T15:35:16.790720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T15:35:16.943341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
active 9587
99.8%
suspended 21
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 9629
16.7%
A 9587
16.6%
c 9587
16.6%
t 9587
16.6%
i 9587
16.6%
v 9587
16.6%
d 42
 
0.1%
S 21
 
< 0.1%
u 21
 
< 0.1%
s 21
 
< 0.1%
Other values (2) 42
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9629
16.7%
A 9587
16.6%
c 9587
16.6%
t 9587
16.6%
i 9587
16.6%
v 9587
16.6%
d 42
 
0.1%
S 21
 
< 0.1%
u 21
 
< 0.1%
s 21
 
< 0.1%
Other values (2) 42
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9629
16.7%
A 9587
16.6%
c 9587
16.6%
t 9587
16.6%
i 9587
16.6%
v 9587
16.6%
d 42
 
0.1%
S 21
 
< 0.1%
u 21
 
< 0.1%
s 21
 
< 0.1%
Other values (2) 42
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9629
16.7%
A 9587
16.6%
c 9587
16.6%
t 9587
16.6%
i 9587
16.6%
v 9587
16.6%
d 42
 
0.1%
S 21
 
< 0.1%
u 21
 
< 0.1%
s 21
 
< 0.1%
Other values (2) 42
 
0.1%
Distinct6017
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
2024-05-06T15:35:17.195638image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.704205
Min length15

Characters and Unicode

Total characters150886
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5477 ?
Unique (%)57.0%

Sample

1st row26-06-2021 18:12
2nd row15-12-2021 08:13
3rd row04-02-2024 09:34
4th row15-10-2023 02:19
5th row11-03-2024 00:34
ValueCountFrequency (%)
never 2842
 
12.9%
in 2842
 
12.9%
logged 2842
 
12.9%
02-05-2024 1085
 
4.9%
03-05-2024 223
 
1.0%
01-05-2024 132
 
0.6%
01-11-2018 87
 
0.4%
20-03-2024 84
 
0.4%
29-04-2024 70
 
0.3%
30-04-2024 69
 
0.3%
Other values (2431) 11782
53.4%
2024-05-06T15:35:17.654442image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23178
15.4%
2 22760
15.1%
- 13532
 
9.0%
12450
 
8.3%
1 8883
 
5.9%
e 8526
 
5.7%
4 7900
 
5.2%
: 6766
 
4.5%
3 5887
 
3.9%
g 5684
 
3.8%
Other values (13) 35320
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23178
15.4%
2 22760
15.1%
- 13532
 
9.0%
12450
 
8.3%
1 8883
 
5.9%
e 8526
 
5.7%
4 7900
 
5.2%
: 6766
 
4.5%
3 5887
 
3.9%
g 5684
 
3.8%
Other values (13) 35320
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23178
15.4%
2 22760
15.1%
- 13532
 
9.0%
12450
 
8.3%
1 8883
 
5.9%
e 8526
 
5.7%
4 7900
 
5.2%
: 6766
 
4.5%
3 5887
 
3.9%
g 5684
 
3.8%
Other values (13) 35320
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23178
15.4%
2 22760
15.1%
- 13532
 
9.0%
12450
 
8.3%
1 8883
 
5.9%
e 8526
 
5.7%
4 7900
 
5.2%
: 6766
 
4.5%
3 5887
 
3.9%
g 5684
 
3.8%
Other values (13) 35320
23.4%

Recovery Email
Text

MISSING 

Distinct859
Distinct (%)98.4%
Missing8735
Missing (%)90.9%
Memory size75.2 KiB
2024-05-06T15:35:17.887820image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length36
Median length32
Mean length25.187858
Min length14

Characters and Unicode

Total characters21989
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique845 ?
Unique (%)96.8%

Sample

1st rowjaynthkumarchitrada@gmail.com
2nd rowvassuchappa94822@gmail.com
3rd rowkillamsettytanuja@gmail.com
4th rownamballajitendra@gmail.com
5th rowrevathisadagana25@gmail.com
ValueCountFrequency (%)
kamesh3410@gmail.com 2
 
0.2%
venkey1704@gmail.com 2
 
0.2%
talk2hemasundar@gmail.com 2
 
0.2%
cbabuv@gmail.com 2
 
0.2%
youghander444@gmail.com 2
 
0.2%
sguru7603@gmail.com 2
 
0.2%
jayavardhanarao.mca@gmail.com 2
 
0.2%
subuddi_nagaraju@yahoo.com 2
 
0.2%
duppalaazadin@gmail.com 2
 
0.2%
balaram_bora@rediffmail.com 2
 
0.2%
Other values (849) 853
97.7%
2024-05-06T15:35:18.289711image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3330
15.1%
m 2119
 
9.6%
i 1726
 
7.8%
l 1254
 
5.7%
o 1180
 
5.4%
g 1034
 
4.7%
. 1002
 
4.6%
c 975
 
4.4%
@ 873
 
4.0%
r 825
 
3.8%
Other values (44) 7671
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21989
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3330
15.1%
m 2119
 
9.6%
i 1726
 
7.8%
l 1254
 
5.7%
o 1180
 
5.4%
g 1034
 
4.7%
. 1002
 
4.6%
c 975
 
4.4%
@ 873
 
4.0%
r 825
 
3.8%
Other values (44) 7671
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21989
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3330
15.1%
m 2119
 
9.6%
i 1726
 
7.8%
l 1254
 
5.7%
o 1180
 
5.4%
g 1034
 
4.7%
. 1002
 
4.6%
c 975
 
4.4%
@ 873
 
4.0%
r 825
 
3.8%
Other values (44) 7671
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21989
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3330
15.1%
m 2119
 
9.6%
i 1726
 
7.8%
l 1254
 
5.7%
o 1180
 
5.4%
g 1034
 
4.7%
. 1002
 
4.6%
c 975
 
4.4%
@ 873
 
4.0%
r 825
 
3.8%
Other values (44) 7671
34.9%

Home Secondary Email
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing9603
Missing (%)99.9%
Memory size75.2 KiB
2024-05-06T15:35:18.478234image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length27
Median length20
Mean length21.6
Min length20

Characters and Unicode

Total characters108
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowdrao.mtech@gmail.com
2nd rowkamesh3410@gmail.com
3rd rowyugndasari@gmail.com
4th rowchigurukota@gmail.com
5th rowsateeshkumargudla@gmail.com
ValueCountFrequency (%)
drao.mtech@gmail.com 1
20.0%
kamesh3410@gmail.com 1
20.0%
yugndasari@gmail.com 1
20.0%
chigurukota@gmail.com 1
20.0%
sateeshkumargudla@gmail.com 1
20.0%
2024-05-06T15:35:18.805332image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13
12.0%
m 13
12.0%
g 8
 
7.4%
o 7
 
6.5%
c 7
 
6.5%
i 7
 
6.5%
. 6
 
5.6%
l 6
 
5.6%
u 5
 
4.6%
@ 5
 
4.6%
Other values (13) 31
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13
12.0%
m 13
12.0%
g 8
 
7.4%
o 7
 
6.5%
c 7
 
6.5%
i 7
 
6.5%
. 6
 
5.6%
l 6
 
5.6%
u 5
 
4.6%
@ 5
 
4.6%
Other values (13) 31
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13
12.0%
m 13
12.0%
g 8
 
7.4%
o 7
 
6.5%
c 7
 
6.5%
i 7
 
6.5%
. 6
 
5.6%
l 6
 
5.6%
u 5
 
4.6%
@ 5
 
4.6%
Other values (13) 31
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13
12.0%
m 13
12.0%
g 8
 
7.4%
o 7
 
6.5%
c 7
 
6.5%
i 7
 
6.5%
. 6
 
5.6%
l 6
 
5.6%
u 5
 
4.6%
@ 5
 
4.6%
Other values (13) 31
28.7%

Work Secondary Email
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing9604
Missing (%)> 99.9%
Memory size75.2 KiB
2024-05-06T15:35:18.970922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length27
Median length23
Mean length22.5
Min length17

Characters and Unicode

Total characters90
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowdtvdrao@gmail.com
2nd rowanupjha675@gmail.com
3rd rowbyomakeshdash2000@gmail.com
4th rowsbhaskar166@rediffmail.com
ValueCountFrequency (%)
dtvdrao@gmail.com 1
25.0%
anupjha675@gmail.com 1
25.0%
byomakeshdash2000@gmail.com 1
25.0%
sbhaskar166@rediffmail.com 1
25.0%
2024-05-06T15:35:19.267130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11
 
12.2%
m 9
 
10.0%
o 6
 
6.7%
i 5
 
5.6%
s 4
 
4.4%
c 4
 
4.4%
. 4
 
4.4%
l 4
 
4.4%
d 4
 
4.4%
@ 4
 
4.4%
Other values (20) 35
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11
 
12.2%
m 9
 
10.0%
o 6
 
6.7%
i 5
 
5.6%
s 4
 
4.4%
c 4
 
4.4%
. 4
 
4.4%
l 4
 
4.4%
d 4
 
4.4%
@ 4
 
4.4%
Other values (20) 35
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11
 
12.2%
m 9
 
10.0%
o 6
 
6.7%
i 5
 
5.6%
s 4
 
4.4%
c 4
 
4.4%
. 4
 
4.4%
l 4
 
4.4%
d 4
 
4.4%
@ 4
 
4.4%
Other values (20) 35
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11
 
12.2%
m 9
 
10.0%
o 6
 
6.7%
i 5
 
5.6%
s 4
 
4.4%
c 4
 
4.4%
. 4
 
4.4%
l 4
 
4.4%
d 4
 
4.4%
@ 4
 
4.4%
Other values (20) 35
38.9%
Distinct295
Distinct (%)21.5%
Missing8236
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Mean9.5077638 × 1011
Minimum9.16006 × 1011
Maximum9.77982 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.2 KiB
2024-05-06T15:35:19.449611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum9.16006 × 1011
5-th percentile9.16303 × 1011
Q19.17672 × 1011
median9.18791 × 1011
Q39.19399 × 1011
95-th percentile9.19886 × 1011
Maximum9.77982 × 1012
Range8.863814 × 1012
Interquartile range (IQR)1.727 × 109

Descriptive statistics

Standard deviation5.3416374 × 1011
Coefficient of variation (CV)0.56181848
Kurtosis270.38747
Mean9.5077638 × 1011
Median Absolute Deviation (MAD)7.6 × 108
Skewness16.492145
Sum1.3044652 × 1015
Variance2.853309 × 1023
MonotonicityNot monotonic
2024-05-06T15:35:19.632154image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.16303 × 101130
 
0.3%
9.16304 × 101128
 
0.3%
9.19391 × 101124
 
0.2%
9.19347 × 101122
 
0.2%
9.19492 × 101122
 
0.2%
9.19441 × 101119
 
0.2%
9.1939 × 101119
 
0.2%
9.16306 × 101118
 
0.2%
9.19348 × 101118
 
0.2%
9.19392 × 101116
 
0.2%
Other values (285) 1156
 
12.0%
(Missing) 8236
85.7%
ValueCountFrequency (%)
9.16006 × 10111
 
< 0.1%
9.16281 × 101115
0.2%
9.16282 × 10118
 
0.1%
9.163 × 101113
0.1%
9.16301 × 101115
0.2%
9.16302 × 101116
0.2%
9.16303 × 101130
0.3%
9.16304 × 101128
0.3%
9.16305 × 101116
0.2%
9.16306 × 101118
0.2%
ValueCountFrequency (%)
9.77982 × 10122
 
< 0.1%
9.77981 × 10123
< 0.1%
9.59953 × 10111
 
< 0.1%
9.1999 × 10112
 
< 0.1%
9.19989 × 10113
< 0.1%
9.19986 × 10116
0.1%
9.19967 × 10112
 
< 0.1%
9.19966 × 10117
0.1%
9.19964 × 10113
< 0.1%
9.19963 × 10112
 
< 0.1%

Work Phone
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing9589
Missing (%)99.8%
Memory size75.2 KiB
2024-05-06T15:35:19.809677image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length21
Median length10
Mean length10.578947
Min length9

Characters and Unicode

Total characters201
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row9440955436
2nd row7970640129
3rd row9502866357
4th row6281236826
5th row98666903493
ValueCountFrequency (%)
9440955436 1
 
5.3%
8985994892 1
 
5.3%
7970640129 1
 
5.3%
9502866357 1
 
5.3%
6281236826 1
 
5.3%
98666903493 1
 
5.3%
9000287382 1
 
5.3%
7978568017 1
 
5.3%
6302631625 1
 
5.3%
clerk.cse 1
 
5.3%
Other values (9) 9
47.4%
2024-05-06T15:35:20.123123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 29
14.4%
6 22
10.9%
8 20
10.0%
0 18
9.0%
5 16
8.0%
7 16
8.0%
3 15
7.5%
2 14
7.0%
4 14
7.0%
1 7
 
3.5%
Other values (17) 30
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 29
14.4%
6 22
10.9%
8 20
10.0%
0 18
9.0%
5 16
8.0%
7 16
8.0%
3 15
7.5%
2 14
7.0%
4 14
7.0%
1 7
 
3.5%
Other values (17) 30
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 29
14.4%
6 22
10.9%
8 20
10.0%
0 18
9.0%
5 16
8.0%
7 16
8.0%
3 15
7.5%
2 14
7.0%
4 14
7.0%
1 7
 
3.5%
Other values (17) 30
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 29
14.4%
6 22
10.9%
8 20
10.0%
0 18
9.0%
5 16
8.0%
7 16
8.0%
3 15
7.5%
2 14
7.0%
4 14
7.0%
1 7
 
3.5%
Other values (17) 30
14.9%

Home Phone
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing9586
Missing (%)99.8%
Memory size75.2 KiB
2024-05-06T15:35:20.299652image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length29
Median length10
Mean length12
Min length10

Characters and Unicode

Total characters264
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row‭+91 94405 50343‬
2nd row‭+91 96761 91396‬
3rd row9959658266
4th row9949688627
5th row9391040373
ValueCountFrequency (%)
91 2
 
6.9%
‭+91 2
 
6.9%
8247317098 1
 
3.4%
9989819850 1
 
3.4%
91396‬ 1
 
3.4%
96761 1
 
3.4%
9959658266 1
 
3.4%
9949688627 1
 
3.4%
9391040373 1
 
3.4%
46298 1
 
3.4%
Other values (17) 17
58.6%
2024-05-06T15:35:20.643762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 52
19.7%
8 30
11.4%
4 28
10.6%
1 24
9.1%
0 23
8.7%
6 22
8.3%
3 18
 
6.8%
5 15
 
5.7%
7 14
 
5.3%
2 12
 
4.5%
Other values (6) 26
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 52
19.7%
8 30
11.4%
4 28
10.6%
1 24
9.1%
0 23
8.7%
6 22
8.3%
3 18
 
6.8%
5 15
 
5.7%
7 14
 
5.3%
2 12
 
4.5%
Other values (6) 26
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 52
19.7%
8 30
11.4%
4 28
10.6%
1 24
9.1%
0 23
8.7%
6 22
8.3%
3 18
 
6.8%
5 15
 
5.7%
7 14
 
5.3%
2 12
 
4.5%
Other values (6) 26
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 52
19.7%
8 30
11.4%
4 28
10.6%
1 24
9.1%
0 23
8.7%
6 22
8.3%
3 18
 
6.8%
5 15
 
5.7%
7 14
 
5.3%
2 12
 
4.5%
Other values (6) 26
9.8%

Mobile Phone
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct63
Distinct (%)100.0%
Missing9545
Missing (%)99.3%
Infinite0
Infinite (%)0.0%
Mean8.6268806 × 109
Minimum1.2345679 × 109
Maximum9.9630428 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.2 KiB
2024-05-06T15:35:20.835249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.2345679 × 109
5-th percentile6.4085675 × 109
Q17.893445 × 109
median9.3474965 × 109
Q39.6184903 × 109
95-th percentile9.9457465 × 109
Maximum9.9630428 × 109
Range8.7284749 × 109
Interquartile range (IQR)1.7250453 × 109

Descriptive statistics

Standard deviation1.4146123 × 109
Coefficient of variation (CV)0.16397727
Kurtosis10.773712
Mean8.6268806 × 109
Median Absolute Deviation (MAD)5.6494554 × 108
Skewness-2.4995538
Sum5.4349348 × 1011
Variance2.0011281 × 1018
MonotonicityNot monotonic
2024-05-06T15:35:21.023746image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9440053646 1
 
< 0.1%
9618760185 1
 
< 0.1%
9490084529 1
 
< 0.1%
7989777611 1
 
< 0.1%
9652274096 1
 
< 0.1%
7901691279 1
 
< 0.1%
7386645435 1
 
< 0.1%
8500364408 1
 
< 0.1%
9491991591 1
 
< 0.1%
9912442010 1
 
< 0.1%
Other values (53) 53
 
0.6%
(Missing) 9545
99.3%
ValueCountFrequency (%)
1234567890 1
< 0.1%
6303319991 1
< 0.1%
6304194550 1
< 0.1%
6305402099 1
< 0.1%
7337056571 1
< 0.1%
7382009625 1
< 0.1%
7382070142 1
< 0.1%
7382363926 1
< 0.1%
7382673321 1
< 0.1%
7386184812 1
< 0.1%
ValueCountFrequency (%)
9963042766 1
< 0.1%
9959792951 1
< 0.1%
9959338376 1
< 0.1%
9949446975 1
< 0.1%
9912442010 1
< 0.1%
9908492921 1
< 0.1%
9885351922 1
< 0.1%
9866899361 1
< 0.1%
9849864199 1
< 0.1%
9704913617 1
< 0.1%

Work Address
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9608
Missing (%)100.0%
Memory size75.2 KiB

Home Address
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9607
Missing (%)> 99.9%
Memory size75.2 KiB
2024-05-06T15:35:21.183289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length105
Median length105
Mean length105
Min length105

Characters and Unicode

Total characters105
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rows/o.a.v.ramana d-no.8-3-25/2 nearmarket amadalavalasa srikakulam-532185.
ValueCountFrequency (%)
s/o.a.v.ramana 1
20.0%
d-no.8-3-25/2 1
20.0%
nearmarket 1
20.0%
amadalavalasa 1
20.0%
srikakulam-532185 1
20.0%
2024-05-06T15:35:21.481521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
35.2%
a 15
14.3%
. 5
 
4.8%
- 4
 
3.8%
r 4
 
3.8%
m 4
 
3.8%
2 3
 
2.9%
l 3
 
2.9%
k 3
 
2.9%
5 3
 
2.9%
Other values (13) 24
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
37
35.2%
a 15
14.3%
. 5
 
4.8%
- 4
 
3.8%
r 4
 
3.8%
m 4
 
3.8%
2 3
 
2.9%
l 3
 
2.9%
k 3
 
2.9%
5 3
 
2.9%
Other values (13) 24
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
37
35.2%
a 15
14.3%
. 5
 
4.8%
- 4
 
3.8%
r 4
 
3.8%
m 4
 
3.8%
2 3
 
2.9%
l 3
 
2.9%
k 3
 
2.9%
5 3
 
2.9%
Other values (13) 24
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
37
35.2%
a 15
14.3%
. 5
 
4.8%
- 4
 
3.8%
r 4
 
3.8%
m 4
 
3.8%
2 3
 
2.9%
l 3
 
2.9%
k 3
 
2.9%
5 3
 
2.9%
Other values (13) 24
22.9%

Employee ID
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing9603
Missing (%)99.9%
Memory size75.2 KiB
2024-05-06T15:35:21.633084image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.8
Min length9

Characters and Unicode

Total characters49
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row15A55A0501
2nd rowA5CSE00T05
3rd rowA05CSE081
4th row14A51A0507
5th row10131A0216
ValueCountFrequency (%)
15a55a0501 1
20.0%
a5cse00t05 1
20.0%
a05cse081 1
20.0%
14a51a0507 1
20.0%
10131a0216 1
20.0%
2024-05-06T15:35:21.923308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11
22.4%
1 9
18.4%
5 9
18.4%
A 7
14.3%
C 2
 
4.1%
S 2
 
4.1%
E 2
 
4.1%
T 1
 
2.0%
8 1
 
2.0%
4 1
 
2.0%
Other values (4) 4
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11
22.4%
1 9
18.4%
5 9
18.4%
A 7
14.3%
C 2
 
4.1%
S 2
 
4.1%
E 2
 
4.1%
T 1
 
2.0%
8 1
 
2.0%
4 1
 
2.0%
Other values (4) 4
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11
22.4%
1 9
18.4%
5 9
18.4%
A 7
14.3%
C 2
 
4.1%
S 2
 
4.1%
E 2
 
4.1%
T 1
 
2.0%
8 1
 
2.0%
4 1
 
2.0%
Other values (4) 4
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11
22.4%
1 9
18.4%
5 9
18.4%
A 7
14.3%
C 2
 
4.1%
S 2
 
4.1%
E 2
 
4.1%
T 1
 
2.0%
8 1
 
2.0%
4 1
 
2.0%
Other values (4) 4
 
8.2%

Employee Type
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing9605
Missing (%)> 99.9%
Memory size75.2 KiB
2024-05-06T15:35:22.059975image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.6666667
Min length7

Characters and Unicode

Total characters23
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowFull Time
2nd rowRegular
3rd rowRegular
ValueCountFrequency (%)
regular 2
50.0%
full 1
25.0%
time 1
25.0%
2024-05-06T15:35:22.356183image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 4
17.4%
e 3
13.0%
u 3
13.0%
R 2
8.7%
g 2
8.7%
a 2
8.7%
r 2
8.7%
F 1
 
4.3%
1
 
4.3%
T 1
 
4.3%
Other values (2) 2
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 4
17.4%
e 3
13.0%
u 3
13.0%
R 2
8.7%
g 2
8.7%
a 2
8.7%
r 2
8.7%
F 1
 
4.3%
1
 
4.3%
T 1
 
4.3%
Other values (2) 2
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 4
17.4%
e 3
13.0%
u 3
13.0%
R 2
8.7%
g 2
8.7%
a 2
8.7%
r 2
8.7%
F 1
 
4.3%
1
 
4.3%
T 1
 
4.3%
Other values (2) 2
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 4
17.4%
e 3
13.0%
u 3
13.0%
R 2
8.7%
g 2
8.7%
a 2
8.7%
r 2
8.7%
F 1
 
4.3%
1
 
4.3%
T 1
 
4.3%
Other values (2) 2
8.7%

Employee Title
Text

MISSING 

Distinct3
Distinct (%)50.0%
Missing9602
Missing (%)99.9%
Memory size75.2 KiB
2024-05-06T15:35:22.489824image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length19
Median length7
Mean length11
Min length7

Characters and Unicode

Total characters66
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)16.7%

Sample

1st rowStudent
2nd rowHOD CSE
3rd rowAssociate Professor
4th rowAssociate Professor
5th rowStudent
ValueCountFrequency (%)
student 3
33.3%
associate 2
22.2%
professor 2
22.2%
hod 1
 
11.1%
cse 1
 
11.1%
2024-05-06T15:35:22.775041image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 8
12.1%
t 8
12.1%
e 7
10.6%
o 6
 
9.1%
S 4
 
6.1%
r 4
 
6.1%
3
 
4.5%
u 3
 
4.5%
d 3
 
4.5%
n 3
 
4.5%
Other values (11) 17
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 8
12.1%
t 8
12.1%
e 7
10.6%
o 6
 
9.1%
S 4
 
6.1%
r 4
 
6.1%
3
 
4.5%
u 3
 
4.5%
d 3
 
4.5%
n 3
 
4.5%
Other values (11) 17
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 8
12.1%
t 8
12.1%
e 7
10.6%
o 6
 
9.1%
S 4
 
6.1%
r 4
 
6.1%
3
 
4.5%
u 3
 
4.5%
d 3
 
4.5%
n 3
 
4.5%
Other values (11) 17
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 8
12.1%
t 8
12.1%
e 7
10.6%
o 6
 
9.1%
S 4
 
6.1%
r 4
 
6.1%
3
 
4.5%
u 3
 
4.5%
d 3
 
4.5%
n 3
 
4.5%
Other values (11) 17
25.8%

Manager Email
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9608
Missing (%)100.0%
Memory size75.2 KiB

Department
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing9605
Missing (%)> 99.9%
Memory size75.2 KiB
2024-05-06T15:35:22.925659image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length33
Median length3
Mean length13
Min length3

Characters and Unicode

Total characters39
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowCSE
2nd rowCSE
3rd rowComputer Science and Engineeering
ValueCountFrequency (%)
cse 2
33.3%
computer 1
16.7%
science 1
16.7%
and 1
16.7%
engineeering 1
16.7%
2024-05-06T15:35:23.219872image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6
15.4%
n 5
12.8%
S 3
7.7%
i 3
7.7%
3
7.7%
C 3
7.7%
E 3
7.7%
r 2
 
5.1%
c 2
 
5.1%
g 2
 
5.1%
Other values (7) 7
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6
15.4%
n 5
12.8%
S 3
7.7%
i 3
7.7%
3
7.7%
C 3
7.7%
E 3
7.7%
r 2
 
5.1%
c 2
 
5.1%
g 2
 
5.1%
Other values (7) 7
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6
15.4%
n 5
12.8%
S 3
7.7%
i 3
7.7%
3
7.7%
C 3
7.7%
E 3
7.7%
r 2
 
5.1%
c 2
 
5.1%
g 2
 
5.1%
Other values (7) 7
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6
15.4%
n 5
12.8%
S 3
7.7%
i 3
7.7%
3
7.7%
C 3
7.7%
E 3
7.7%
r 2
 
5.1%
c 2
 
5.1%
g 2
 
5.1%
Other values (7) 7
17.9%

Cost Center
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9608
Missing (%)100.0%
Memory size75.2 KiB

2sv Enrolled [READ ONLY]
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
False
9504 
True
 
104
ValueCountFrequency (%)
False 9504
98.9%
True 104
 
1.1%
2024-05-06T15:35:23.380439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

2sv Enforced [READ ONLY]
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
False
9608 
ValueCountFrequency (%)
False 9608
100.0%
2024-05-06T15:35:23.500091image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Building ID
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9608
Missing (%)100.0%
Memory size75.2 KiB

Floor Name
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9608
Missing (%)100.0%
Memory size75.2 KiB

Floor Section
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9608
Missing (%)100.0%
Memory size75.2 KiB
Distinct160
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
2024-05-06T15:35:23.634757image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.5430891
Min length5

Characters and Unicode

Total characters53258
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)0.9%

Sample

1st row0.0GB
2nd row0.01GB
3rd row0.0GB
4th row0.0GB
5th row0.02GB
ValueCountFrequency (%)
0.0gb 4118
42.9%
0.09gb 739
 
7.7%
0.01gb 614
 
6.4%
0.06gb 519
 
5.4%
0.02gb 420
 
4.4%
0.03gb 374
 
3.9%
0.05gb 329
 
3.4%
0.07gb 277
 
2.9%
0.11gb 267
 
2.8%
0.08gb 253
 
2.6%
Other values (150) 1698
17.7%
2024-05-06T15:35:24.105500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17411
32.7%
. 9608
18.0%
G 9608
18.0%
B 9608
18.0%
1 2101
 
3.9%
2 1020
 
1.9%
9 866
 
1.6%
6 650
 
1.2%
3 649
 
1.2%
5 506
 
1.0%
Other values (3) 1231
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17411
32.7%
. 9608
18.0%
G 9608
18.0%
B 9608
18.0%
1 2101
 
3.9%
2 1020
 
1.9%
9 866
 
1.6%
6 650
 
1.2%
3 649
 
1.2%
5 506
 
1.0%
Other values (3) 1231
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17411
32.7%
. 9608
18.0%
G 9608
18.0%
B 9608
18.0%
1 2101
 
3.9%
2 1020
 
1.9%
9 866
 
1.6%
6 650
 
1.2%
3 649
 
1.2%
5 506
 
1.0%
Other values (3) 1231
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17411
32.7%
. 9608
18.0%
G 9608
18.0%
B 9608
18.0%
1 2101
 
3.9%
2 1020
 
1.9%
9 866
 
1.6%
6 650
 
1.2%
3 649
 
1.2%
5 506
 
1.0%
Other values (3) 1231
 
2.3%
Distinct368
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
2024-05-06T15:35:24.334893image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.1921316
Min length5

Characters and Unicode

Total characters49886
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique216 ?
Unique (%)2.2%

Sample

1st row0.0GB
2nd row0.0GB
3rd row0.03GB
4th row0.0GB
5th row0.0GB
ValueCountFrequency (%)
0.0gb 7699
80.1%
0.01gb 392
 
4.1%
0.02gb 150
 
1.6%
0.03gb 90
 
0.9%
0.04gb 75
 
0.8%
0.05gb 34
 
0.4%
0.21gb 31
 
0.3%
0.17gb 31
 
0.3%
0.06gb 30
 
0.3%
0.1gb 27
 
0.3%
Other values (358) 1049
 
10.9%
2024-05-06T15:35:24.713876image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17860
35.8%
. 9608
19.3%
G 9608
19.3%
B 9608
19.3%
1 951
 
1.9%
2 518
 
1.0%
3 390
 
0.8%
4 296
 
0.6%
6 242
 
0.5%
5 235
 
0.5%
Other values (3) 570
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17860
35.8%
. 9608
19.3%
G 9608
19.3%
B 9608
19.3%
1 951
 
1.9%
2 518
 
1.0%
3 390
 
0.8%
4 296
 
0.6%
6 242
 
0.5%
5 235
 
0.5%
Other values (3) 570
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17860
35.8%
. 9608
19.3%
G 9608
19.3%
B 9608
19.3%
1 951
 
1.9%
2 518
 
1.0%
3 390
 
0.8%
4 296
 
0.6%
6 242
 
0.5%
5 235
 
0.5%
Other values (3) 570
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17860
35.8%
. 9608
19.3%
G 9608
19.3%
B 9608
19.3%
1 951
 
1.9%
2 518
 
1.0%
3 390
 
0.8%
4 296
 
0.6%
6 242
 
0.5%
5 235
 
0.5%
Other values (3) 570
 
1.1%
Distinct184
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
2024-05-06T15:35:25.004068image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.0328893
Min length5

Characters and Unicode

Total characters48356
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)1.6%

Sample

1st row0.0GB
2nd row0.0GB
3rd row0.0GB
4th row0.0GB
5th row0.0GB
ValueCountFrequency (%)
0.0gb 9318
97.0%
0.01gb 23
 
0.2%
0.02gb 19
 
0.2%
0.04gb 10
 
0.1%
0.03gb 9
 
0.1%
0.08gb 8
 
0.1%
0.05gb 5
 
0.1%
0.15gb 4
 
< 0.1%
0.1gb 4
 
< 0.1%
0.07gb 3
 
< 0.1%
Other values (174) 205
 
2.1%
2024-05-06T15:35:25.442928image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18902
39.1%
. 9608
19.9%
G 9608
19.9%
B 9608
19.9%
1 134
 
0.3%
2 97
 
0.2%
4 73
 
0.2%
3 68
 
0.1%
5 65
 
0.1%
6 59
 
0.1%
Other values (3) 134
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18902
39.1%
. 9608
19.9%
G 9608
19.9%
B 9608
19.9%
1 134
 
0.3%
2 97
 
0.2%
4 73
 
0.2%
3 68
 
0.1%
5 65
 
0.1%
6 59
 
0.1%
Other values (3) 134
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18902
39.1%
. 9608
19.9%
G 9608
19.9%
B 9608
19.9%
1 134
 
0.3%
2 97
 
0.2%
4 73
 
0.2%
3 68
 
0.1%
5 65
 
0.1%
6 59
 
0.1%
Other values (3) 134
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18902
39.1%
. 9608
19.9%
G 9608
19.9%
B 9608
19.9%
1 134
 
0.3%
2 97
 
0.2%
4 73
 
0.2%
3 68
 
0.1%
5 65
 
0.1%
6 59
 
0.1%
Other values (3) 134
 
0.3%

Storage limit [READ ONLY]
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
--
9608 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters28824
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row --
2nd row --
3rd row --
4th row --
5th row --

Common Values

ValueCountFrequency (%)
-- 9608
100.0%

Length

2024-05-06T15:35:25.606487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T15:35:25.733151image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
9608
100.0%

Most occurring characters

ValueCountFrequency (%)
- 19216
66.7%
9608
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 19216
66.7%
9608
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 19216
66.7%
9608
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 19216
66.7%
9608
33.3%
Distinct498
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
2024-05-06T15:35:25.940595image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.5687968
Min length5

Characters and Unicode

Total characters53505
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique307 ?
Unique (%)3.2%

Sample

1st row0.0GB
2nd row0.01GB
3rd row0.03GB
4th row0.0GB
5th row0.02GB
ValueCountFrequency (%)
0.0gb 3916
40.8%
0.09gb 693
 
7.2%
0.01gb 498
 
5.2%
0.06gb 477
 
5.0%
0.02gb 352
 
3.7%
0.03gb 322
 
3.4%
0.05gb 288
 
3.0%
0.07gb 240
 
2.5%
0.11gb 219
 
2.3%
0.08gb 197
 
2.1%
Other values (488) 2406
25.0%
2024-05-06T15:35:26.389362image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16336
30.5%
. 9608
18.0%
G 9608
18.0%
B 9608
18.0%
1 2118
 
4.0%
2 1183
 
2.2%
9 994
 
1.9%
3 890
 
1.7%
6 823
 
1.5%
5 682
 
1.3%
Other values (3) 1655
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16336
30.5%
. 9608
18.0%
G 9608
18.0%
B 9608
18.0%
1 2118
 
4.0%
2 1183
 
2.2%
9 994
 
1.9%
3 890
 
1.7%
6 823
 
1.5%
5 682
 
1.3%
Other values (3) 1655
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16336
30.5%
. 9608
18.0%
G 9608
18.0%
B 9608
18.0%
1 2118
 
4.0%
2 1183
 
2.2%
9 994
 
1.9%
3 890
 
1.7%
6 823
 
1.5%
5 682
 
1.3%
Other values (3) 1655
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16336
30.5%
. 9608
18.0%
G 9608
18.0%
B 9608
18.0%
1 2118
 
4.0%
2 1183
 
2.2%
9 994
 
1.9%
3 890
 
1.7%
6 823
 
1.5%
5 682
 
1.3%
Other values (3) 1655
 
3.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
False
8994 
True
 
614
ValueCountFrequency (%)
False 8994
93.6%
True 614
 
6.4%
2024-05-06T15:35:26.553952image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

New Status [UPLOAD ONLY]
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9608
Missing (%)100.0%
Memory size75.2 KiB
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
False
9608 
ValueCountFrequency (%)
False 9608
100.0%
2024-05-06T15:35:26.668647image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Gemini Limit Status [READ ONLY]
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
NOT_APPROACHING_LIMIT
9608 

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters201768
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOT_APPROACHING_LIMIT
2nd rowNOT_APPROACHING_LIMIT
3rd rowNOT_APPROACHING_LIMIT
4th rowNOT_APPROACHING_LIMIT
5th rowNOT_APPROACHING_LIMIT

Common Values

ValueCountFrequency (%)
NOT_APPROACHING_LIMIT 9608
100.0%

Length

2024-05-06T15:35:26.784339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T15:35:26.923963image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
not_approaching_limit 9608
100.0%

Most occurring characters

ValueCountFrequency (%)
I 28824
14.3%
N 19216
9.5%
O 19216
9.5%
T 19216
9.5%
_ 19216
9.5%
A 19216
9.5%
P 19216
9.5%
R 9608
 
4.8%
C 9608
 
4.8%
H 9608
 
4.8%
Other values (3) 28824
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 28824
14.3%
N 19216
9.5%
O 19216
9.5%
T 19216
9.5%
_ 19216
9.5%
A 19216
9.5%
P 19216
9.5%
R 9608
 
4.8%
C 9608
 
4.8%
H 9608
 
4.8%
Other values (3) 28824
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 28824
14.3%
N 19216
9.5%
O 19216
9.5%
T 19216
9.5%
_ 19216
9.5%
A 19216
9.5%
P 19216
9.5%
R 9608
 
4.8%
C 9608
 
4.8%
H 9608
 
4.8%
Other values (3) 28824
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 28824
14.3%
N 19216
9.5%
O 19216
9.5%
T 19216
9.5%
_ 19216
9.5%
A 19216
9.5%
P 19216
9.5%
R 9608
 
4.8%
C 9608
 
4.8%
H 9608
 
4.8%
Other values (3) 28824
14.3%

Gemini Last Usage [READ ONLY]
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.2 KiB
-
9608 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9608
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 9608
100.0%

Length

2024-05-06T15:35:27.035666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T15:35:27.167315image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
9608
100.0%

Most occurring characters

ValueCountFrequency (%)
- 9608
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 9608
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 9608
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 9608
100.0%

Interactions

2024-05-06T15:35:11.698658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-05-06T15:35:11.347168image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-05-06T15:35:11.860197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-05-06T15:35:11.531676image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2024-05-06T15:35:27.263025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2sv Enrolled [READ ONLY]Change Password at Next Sign-InMobile PhoneRecovery Phone [MUST BE IN THE E.164 FORMAT]Status [READ ONLY]
2sv Enrolled [READ ONLY]1.0000.023NaN-0.0250.000
Change Password at Next Sign-In0.0231.000NaN0.0010.000
Mobile PhoneNaNNaN1.000-0.1201.000
Recovery Phone [MUST BE IN THE E.164 FORMAT]-0.0250.001-0.1201.0000.000
Status [READ ONLY]0.0000.0001.0000.0001.000

Missing values

2024-05-06T15:35:12.171365image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-06T15:35:12.981230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-06T15:35:13.560419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

First Name [Required]Last Name [Required]Email Address [Required]Password [Required]Password Hash Function [UPLOAD ONLY]Org Unit Path [Required]New Primary Email [UPLOAD ONLY]Status [READ ONLY]Last Sign In [READ ONLY]Recovery EmailHome Secondary EmailWork Secondary EmailRecovery Phone [MUST BE IN THE E.164 FORMAT]Work PhoneHome PhoneMobile PhoneWork AddressHome AddressEmployee IDEmployee TypeEmployee TitleManager EmailDepartmentCost Center2sv Enrolled [READ ONLY]2sv Enforced [READ ONLY]Building IDFloor NameFloor SectionEmail Usage [READ ONLY]Drive Usage [READ ONLY]Photos Usage [READ ONLY]Storage limit [READ ONLY]Storage Used [READ ONLY]Change Password at Next Sign-InNew Status [UPLOAD ONLY]Advanced Protection Program enrollmentGemini Limit Status [READ ONLY]Gemini Last Usage [READ ONLY]
08555011862Pisini Mounika08555011862@adityatekkali.edu.in****NaN/NaNActive26-06-2021 18:12NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.0GB0.0GB0.0GB--0.0GBFalseNaNFalseNOT_APPROACHING_LIMIT-
19182018896Raj Kumar potnuru09182018896@adityatekkali.edu.in****NaN/NaNSuspended15-12-2021 08:13NaNNaNNaN9.191820e+11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.01GB0.0GB0.0GB--0.01GBFalseNaNFalseNOT_APPROACHING_LIMIT-
21ADIREDDI LOKESH20a55a0301@adityatekkali.edu.in****NaN/NaNActive04-02-2024 09:34NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.0GB0.03GB0.0GB--0.03GBFalseNaNFalseNOT_APPROACHING_LIMIT-
310BUDDA SAI KIRAN20a55a0310@adityatekkali.edu.in****NaN/NaNActive15-10-2023 02:19NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.0GB0.0GB0.0GB--0.0GBFalseNaNFalseNOT_APPROACHING_LIMIT-
411BUNGA KALYANRAM20a55a0311@adityatekkali.edu.in****NaN/NaNActive11-03-2024 00:34NaNNaNNaN9.163010e+11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.02GB0.0GB0.0GB--0.02GBFalseNaNFalseNOT_APPROACHING_LIMIT-
512CHALUMURI SAI KIRAN20a55a0312@adityatekkali.edu.in****NaN/NaNActive01-03-2024 09:01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.01GB0.0GB0.0GB--0.01GBFalseNaNFalseNOT_APPROACHING_LIMIT-
613CHINNI POLA RAO20a55a0313@adityatekkali.edu.in****NaN/NaNActive25-02-2023 05:05NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.0GB0.0GB0.0GB--0.0GBFalseNaNFalseNOT_APPROACHING_LIMIT-
714CHINTAPALLI RAMAKRISHNA20a55a0314@adityatekkali.edu.in****NaN/NaNActive25-12-2023 06:01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.01GB0.07GB0.0GB--0.08GBFalseNaNFalseNOT_APPROACHING_LIMIT-
814A51A0160M Santosh Kumar14a51a0160@adityatekkali.edu.in****NaN/NaNActiveNever logged inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.0GB0.0GB0.0GB--0.0GBFalseNaNFalseNOT_APPROACHING_LIMIT-
915CHITRADA PRAVEEN KUMAR20a55a0315@adityatekkali.edu.in****NaN/NaNActive27-12-2023 23:48jaynthkumarchitrada@gmail.comNaNNaN9.175700e+11NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.0GB0.0GB0.0GB--0.0GBFalseNaNFalseNOT_APPROACHING_LIMIT-
First Name [Required]Last Name [Required]Email Address [Required]Password [Required]Password Hash Function [UPLOAD ONLY]Org Unit Path [Required]New Primary Email [UPLOAD ONLY]Status [READ ONLY]Last Sign In [READ ONLY]Recovery EmailHome Secondary EmailWork Secondary EmailRecovery Phone [MUST BE IN THE E.164 FORMAT]Work PhoneHome PhoneMobile PhoneWork AddressHome AddressEmployee IDEmployee TypeEmployee TitleManager EmailDepartmentCost Center2sv Enrolled [READ ONLY]2sv Enforced [READ ONLY]Building IDFloor NameFloor SectionEmail Usage [READ ONLY]Drive Usage [READ ONLY]Photos Usage [READ ONLY]Storage limit [READ ONLY]Storage Used [READ ONLY]Change Password at Next Sign-InNew Status [UPLOAD ONLY]Advanced Protection Program enrollmentGemini Limit Status [READ ONLY]Gemini Last Usage [READ ONLY]
9598YELLUMAHANTHI SUMANTH PATROECE16a51a04h7@adityatekkali.edu.in****NaN/NaNActive01-11-2018 01:20NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.09GB0.0GB0.0GB--0.09GBFalseNaNFalseNOT_APPROACHING_LIMIT-
9599YENDU DILIP22A51A12J622a51a12j6@adityatekkali.edu.in****NaN/NaNActive20-04-2024 06:31NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.02GB0.0GB0.0GB--0.02GBFalseNaNFalseNOT_APPROACHING_LIMIT-
9600YENDUVA PAVAN SAI22A51A12J722a51a12j7@adityatekkali.edu.in****NaN/NaNActive20-01-2024 21:55NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.0GB0.0GB0.0GB--0.0GBFalseNaNFalseNOT_APPROACHING_LIMIT-
9601YENNI DHILLESHWARA RAOME16a51a03a8@adityatekkali.edu.in****NaN/NaNActiveNever logged inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.09GB0.0GB0.0GB--0.09GBTrueNaNFalseNOT_APPROACHING_LIMIT-
9602Yenni Dhilleswara RaoCSEyennidil7981@adityatekkali.edu.in****NaN/NaNActiveNever logged inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.0GB0.0GB0.0GB--0.0GBFalseNaNFalseNOT_APPROACHING_LIMIT-
9603YENNI HARISHCSE16a51a05h1@adityatekkali.edu.in****NaN/NaNActiveNever logged inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.1GB0.0GB0.0GB--0.1GBFalseNaNFalseNOT_APPROACHING_LIMIT-
9604YENNI SAI SANKAR GOPALCSE16a51a05e7@adityatekkali.edu.in****NaN/NaNActive04-02-2021 00:37NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.11GB0.0GB0.0GB--0.11GBFalseNaNFalseNOT_APPROACHING_LIMIT-
9605YENUGUTALA CHANIKYAEEE16a51a0284@adityatekkali.edu.in****NaN/NaNActiveNever logged inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.09GB0.0GB0.0GB--0.09GBTrueNaNFalseNOT_APPROACHING_LIMIT-
9606Yogitha BattulaCSEyogithab@adityatekkali.edu.in****NaN/NaNActiveNever logged inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.0GB0.0GB0.0GB--0.0GBFalseNaNFalseNOT_APPROACHING_LIMIT-
9607Younifyaitamradioyounifyaitamradio@adityatekkali.edu.in****NaN/NaNActive24-08-2023 06:35NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN0.02GB0.0GB0.0GB--0.02GBFalseNaNFalseNOT_APPROACHING_LIMIT-